History of the U.S. Climate Divisional Dataset

For many years the Climate Divisional Dataset was the only long-term temporally and spatially complete dataset from which to generate historical climate analyses (1895-2013) for the contiguous United States (CONUS). It was originally developed for climate-division, statewide, regional, national, and population-weighted monitoring of drought, temperature, precipitation, and heating/cooling degree day values. Since the dataset was at the divisional spatial scale, it naturally lent itself to agricultural and hydrological applications.

There are 344 climate divisions in the CONUS. For each climate division, monthly station temperature and precipitation values are computed from the daily observations. The divisional values are weighted by area to compute statewide values and the statewide values are weighted by area to compute regional values. (Karl and Koss, 1984).

In March 2015, historical data for thirteen Alaskan climate divisions were added to the nClimDiv database and will be updated each month with the CONUS nClimDiv data. The Alaska nClimDiv data were created and updated using similar methodology as that for the CONUS, but with a different approach to establishing the underlying climatology. The Alaska data are built upon the 1971-2000 PRISM averages whereas the CONUS values utilize a base climatology derived from the nClimDiv dataset. More information on this new dataset can be access here: Alaska FAQ's

Drd964x Dataset

Traditionally, climate division values have been computed using the monthly values for all of the Cooperative Observer Network (COOP) stations in each division are averaged to compute divisional monthly temperature and precipitation averages/totals. This is valid for values computed from 1931-2013. For the 1895-1930 period, statewide values were computed directly from stations within each state. Divisional values for this early period were computed using a regression technique against the statewide values (Guttman and Quayle, 1996). These values make up the Drd964x division dataset.

nClimDiv Dataset

The nClimDiv dataset is based on the GHCND dataset using a 5km gridded appoach. It is based on a similar station inventory as the Drd964x dataset however, new methodologies are used to compute temperature, precipitation, and drought for United States climate divisions. These new methodologies include the transition to a grid-based calculation, the inclusion of many more stations from the pre-1930s, and the use of NCEI's modern array of quality control algorithms. These have improved the data coverage and the quality of the dataset, while maintaining the current product stream.

The nClimDiv dataset is designed to address the following general issues inherent in the Drd964x dataset:

For the Drd964x dataset, each divisional value from 1931-2013 is simply the arithmetic average of the station data within it, a computational practice that results in a bias when a division is spatially undersampled in a month (e.g., because some stations did not report) or is climatologically inhomogeneous in general (e.g., due to large variations in topography).

For the Drd964x dataset, all divisional values before 1931 stem from state averages published by the U.S. Department of Agriculture (USDA) rather than from actual station observations, producing an artificial discontinuity in both the mean and variance for 1895-1930 (Guttman and Quayle, 1996).

In the Drd964x dataset, many divisions experienced a systematic change in average station location and elevation during the 20th Century, resulting in spurious historical trends in some regions (Keim et al., 2003; Keim et al., 2005; Allard et al., 2009).

The first (and most straightforward) improvement to the nClimDiv dataset involves updating the underlying network of stations, which now includes additional station records and contemporary bias adjustments (i.e., those used in the U.S. Historical Climatology Network version 2; Menne et al., 2009).

The second (and far more extensive) improvement is to the computational methodology, which now addresses topographic and network variability via climatologically aided interpolation (Willmott and Robeson, 1995). The outcome of these improvements is a new divisional dataset that maintains the strengths of its predecessor while providing more robust estimates of areal averages and long-term trends.

The NCEI's Monitoring Branch transitioned from the Drd964x dataset to the more modern the nClimDiv dataset in early 2014. While this transition did not disrupt the current product stream, some variances in temperature and precipitation values may be observed throughout the data record. For example, in general, climate divisions with extensive topography above the average station elevation will be reflected as cooler climatology. An assessment of the major impacts of this transition can be found in Fenimore, et. al, 2011.

In March 2015, historical data for thirteen Alaskan climate divisions were added to the nClimDiv database and will be updated each month with the CONUS nClimDiv data. The Alaska nClimDiv data were created and updated using similar methodology as that for the CONUS, but with a different approach to establishing the underlying climatology. The Alaska data are built upon the 1971-2000 PRISM averages whereas the CONUS values utilize a base climatology derived from the nClimDiv dataset. More information on this new dataset can be access here: Alaska FAQ's

National Temperature Comparison Table

NCEI often expresses a month's, season's or year's temperature anomaly as a rank, or how the period "ranked" among its history (for example, 23rd warmest of 118 on record). Expressing a value as a rank provides an easily-understandable depiction of the relative placement of the month, season or year, but using rankings is very sensitive to even small changes in the values. For example, imagine a footrace with 118 runners. In most cases, many of the runners finish very near to each other ("in a pack"), where the slightest change could result in a "bump" in rank of several positions within the pack. In the same way, annual temperature anomalies feature a few outstanding (warm or cold) years, and a large "pack". Slight changes to any one year can result in a "bump" in rank in the "middle of the pack". This sensitivity to slight changes is one of the criticisms of using the ranking method, despite its known utility for quickly conveying how a single month, season or year compares to others in history.

Discovery Tool

A visualization toolkit was created to help users examine snapshots of both datasets for the comparison period (i.e., through December 2013). The tool allows the user to select criteria which are of interest and investigate the comparisons themselves. Parameters included in the toolkit are temperature, precipitation, degree days and a variety of drought indices. Changes in monthly, seasonal and annual variability can be examined through the use of the interactive time series plots. In addition, slope (trend) values by decade and 30-year period may also be added to the output plots. This allows the user to take a closer look at the behavior of the data at a variety of smaller time scales throughout the record.